[转]Greenplum 资源隔离的原理与源码分析

摘要： 背景 Greenplum是一个MPP的数据仓库系统，最大的优势是水平扩展，而且一个QUERY就能将硬件资源的能力发挥到极致。 但这也是被一些用户诟病的一点，由于一个的QUERY就可能占光全部的硬件资源，因此并发一多的话，query相互之间的资源争抢就比较严重。 Greenplum资源隔

背景

Greenplum是一个MPP的数据仓库系统，最大的优势是水平扩展，而且一个QUERY就能将硬件资源的能力发挥到极致。

但这也是被一些用户诟病的一点，由于一个的QUERY就可能占光全部的硬件资源，因此并发一多的话，query相互之间的资源争抢就比较严重。

Greenplum资源隔离的手段

Greenplum为了下降并发query之间的资源争抢，设计了一套基于resource queue的资源管理方法。

每一个resource queue定义了资源的使用或限制模式，根据用户的用途将用户指派给resource queue，这样就起到了资源管理的目的。

例如将分析师、跑报表的、ETL分为三用户。根据这三类用户的预期资源使用状况，以及任务的优先级，规划三类资源管理的队列。分别将三类用户和三类resource queue绑定，起到资源控制的做用。

resource queue的建立语法

支持的资源隔离类别

• active_statements， 该queue同时能够运行的query数量。
• max_cost，指资源组内全部正在运行的query的评估成本的最大值。
• cost_overcommit，当系统空闲时，是否容许该queue的query总cost超出设定的max_cost。
• min_cost 指低于该值的QUERY不计入该queue 的cost成本，也不排队，而是直接执行。
• priority ， 用于平衡各个QUEUE之间的CPU争抢使用，分为5个等级，每一个等级设定了响应的weight，间隔必定的时间判断使用的资源是否达到了weight，而后对该queue 的query使用pg_usleep进行抑制。
• mem_limit ， 为队列中单个segment query(s)容许的最大statement(s)运行内存。

建立resource queue时必须设置active_statements与max_cost之一。

只有超级用户能建立和修改resource queue。

绑定角色与resource queue

resource queue用法举例

建立两个资源队列，指派给两个用户（一个资源队列能够指派给多个用户）。

postgres=# create resource queue min with (active_statements=3, priority=min); CREATE QUEUE postgres=# create resource queue max with (active_statements=1, priority=max); CREATE QUEUE postgres=# create role max login encrypted password '123' resource queue max; CREATE ROLE postgres=# create role min login encrypted password '123' resource queue min; CREATE ROLE 

Greenplum资源隔离的相关代码

src/include/catalog/pg_resqueue.h

#define PG_RESRCTYPE_ACTIVE_STATEMENTS 1 /* rsqcountlimit: count */ #define PG_RESRCTYPE_MAX_COST 2 /* rsqcostlimit: max_cost */ #define PG_RESRCTYPE_MIN_COST 3 /* rsqignorecostlimit: min_cost */ #define PG_RESRCTYPE_COST_OVERCOMMIT 4 /* rsqovercommit: cost_overcommit*/ /* start of "pg_resourcetype" entries... */ #define PG_RESRCTYPE_PRIORITY 5 /* backoff.c: priority queue */ #define PG_RESRCTYPE_MEMORY_LIMIT 6 /* memquota.c: memory quota */ 

接下来我挑选了CPU的资源调度进行源码的分析，其余的几个本文就不分析了。

CPU的资源隔离

src/backend/postmaster/backoff.c
五个CPU优先级级别，以及对应的weight(可经过gp_adjust_priority函数调整当前query的weight)。

typedef struct PriorityMapping { const char *priorityVal; int weight; } PriorityMapping; const struct PriorityMapping priority_map[] = { {"MAX", 1000000}, {"HIGH", 1000}, {"MEDIUM", 500}, {"LOW", 200}, {"MIN", 100}, /* End of list marker */ {NULL, 0} }; 

单个进程的资源使用统计信息数据结构

/** * This is information that only the current backend ever needs to see. */ typedef struct BackoffBackendLocalEntry { int processId; /* Process Id of backend */ struct rusage startUsage; /* Usage when current statement began. To account for caching of backends. */ struct rusage lastUsage; /* Usage statistics when backend process performed local backoff action */ double lastSleepTime; /* Last sleep time when local backing-off action was performed */ int counter; /* Local counter is used as an approx measure of time */ bool inTick; /* Is backend currently performing tick? - to prevent nested calls */ bool groupingTimeExpired; /* Should backend try to find better leader? */ } BackoffBackendLocalEntry; 

单个segment或master内全部进程共享的资源使用统计信息数据结构

/**
 * There is a backend entry for every backend with a valid backendid on the master and segments. */ typedef struct BackoffBackendSharedEntry { struct StatementId statementId; /* A statement Id. Can be invalid. */ int groupLeaderIndex; /* Who is my leader? */ int groupSize; /* How many in my group ? */ int numFollowers; /* How many followers do I have? */ /* These fields are written by backend and read by sweeper process */ struct timeval lastCheckTime; /* Last time the backend process performed local back-off action. Used to determine inactive backends. */ /* These fields are written to by sweeper and read by backend */ bool noBackoff; /* If set, then no backoff to be performed by this backend */ double targetUsage; /* Current target CPU usage as calculated by sweeper */ bool earlyBackoffExit; /* Sweeper asking backend to stop backing off */ /* These fields are written to and read by sweeper */ bool isActive; /* Sweeper marking backend as active based on lastCheckTime */ int numFollowersActive; /* If backend is a leader, this represents number of followers that are active */ /* These fields are wrtten by backend during init and by manual adjustment */ int weight; /* Weight of this statement */ } BackoffBackendSharedEntry; /* In ms */ #define MIN_SLEEP_THRESHOLD 5000 /* In ms */ #define DEFAULT_SLEEP_TIME 100.0 

经过getrusage()系统调用得到进程的资源使用状况

/* Provide tracing information */ PG_TRACE1(backoff__localcheck, MyBackendId); if (gettimeofday(&currentTime, NULL) < 0) { elog(ERROR, "Unable to execute gettimeofday(). Please disable query prioritization."); } if (getrusage(RUSAGE_SELF, &currentUsage) < 0) { elog(ERROR, "Unable to execute getrusage(). Please disable query prioritization."); } 

资源使用换算

if (!se->noBackoff) { /* How much did the cpu work on behalf of this process - incl user and sys time */ thisProcessTime = TIMEVAL_DIFF_USEC(currentUsage.ru_utime, le->lastUsage.ru_utime) + TIMEVAL_DIFF_USEC(currentUsage.ru_stime, le->lastUsage.ru_stime); /* Absolute cpu time since the last check. This accounts for multiple procs per segment */ totalTime = TIMEVAL_DIFF_USEC(currentTime, se->lastCheckTime); cpuRatio = thisProcessTime / totalTime; cpuRatio = Min(cpuRatio, 1.0); changeFactor = cpuRatio / se->targetUsage; // 和priority的weight有关, // 和参数gp_resqueue_priority_cpucores_per_segment有关, double CPUAvailable = numProcsPerSegment(); 有关, // se->targetUsage = (CPUAvailable) * (se->weight) / activeWeight / gl->numFollowersActive; le->lastSleepTime *= changeFactor; // 计算是否须要sleep if (le->lastSleepTime < DEFAULT_SLEEP_TIME) le->lastSleepTime = DEFAULT_SLEEP_TIME; 

超出MIN_SLEEP_THRESHOLD则进入休眠

memcpy( &le->lastUsage, &currentUsage, sizeof(currentUsage));
                memcpy( &se->lastCheckTime, &currentTime, sizeof(currentTime));

                if (le->lastSleepTime > MIN_SLEEP_THRESHOLD) // 计算是否须要sleep { /* * Sleeping happens in chunks so that the backend may exit early from its sleep if the sweeper requests it to. */ int j =0; long sleepInterval = ((long) gp_resqueue_priority_sweeper_interval) * 1000L; int numIterations = (int) (le->lastSleepTime / sleepInterval); double leftOver = (double) ((long) le->lastSleepTime % sleepInterval); for (j=0;j<numIterations;j++) { /* Sleep a chunk */ pg_usleep(sleepInterval); // 休眠 /* Check for early backoff exit */ if (se->earlyBackoffExit) { le->lastSleepTime = DEFAULT_SLEEP_TIME; /* Minimize sleep time since we may need to recompute from scratch */ break; } } if (j==numIterations) pg_usleep(leftOver); } } 

除了前面的休眠调度，还须要考虑当数据库空闲的时候，应该尽可能使用数据库的资源，那么什么状况下不进入休眠呢？

/**
         * Under certain conditions, we want to avoid backoff. Cases are: * 1. A statement just entered or exited * 2. A statement's weight changed due to user intervention via gp_adjust_priority() * 3. There is no active backend * 4. There is exactly one statement * 5. Total number valid of backends <= number of procs per segment(gp_resqueue_priority_cpucores_per_segment 参数设置) * Case 1 and 2 are approximated by checking if total statement weight changed since last sweeper loop. */ 

如何调整正在执行的query的weight

当正在执行一个query时，若是发现它太占资源，咱们能够动态的设置它的weight。

当一个query正在执行时，能够调整它的priority

postgres=# set gp_debug_resqueue_priority=on; postgres=# set client_min_messages ='debug'; 查询当前的resource queue priority postgres=# select * from gp_toolkit.gp_resq_priority_statement; rqpdatname | rqpusename | rqpsession | rqpcommand | rqppriority | rqpweight | rqpquery ------------+------------+------------+------------+-------------+-----------+-------------------------------------------------------- postgres | digoal | 21 | 1 | MAX | 1000000 | select pg_sleep(1000000) from gp_dist_random('gp_id'); postgres | digoal | 22 | 1 | MAX | 1000000 | select pg_sleep(1000000) from gp_dist_random('gp_id'); postgres | digoal | 23 | 1 | MAX | 1000000 | select pg_sleep(1000000) from gp_dist_random('gp_id'); postgres | digoal | 24 | 1 | MAX | 1000000 | select pg_sleep(1000000) from gp_dist_random('gp_id'); postgres | digoal | 25 | 1 | MAX | 1000000 | select pg_sleep(1000000) from gp_dist_random('gp_id'); postgres | digoal | 26 | 65 | MAX | 1000000 | select * from gp_toolkit.gp_resq_priority_statement; (6 rows) 设置，能够直接设置priority的别名（MIN, MAX, LOW, HIGH, MEDIAM）,或者使用数字设置weight。 postgres=# select gp_adjust_priority(21,1,'MIN'); LOG: changing weight of (21:1) from 1000000 to 100 gp_adjust_priority -------------------- 1 (1 row) postgres=# select * from gp_toolkit.gp_resq_priority_statement; rqpdatname | rqpusename | rqpsession | rqpcommand | rqppriority | rqpweight | rqpquery ------------+------------+------------+------------+-------------+-----------+-------------------------------------------------------- postgres | digoal | 21 | 1 | MIN | 100 | select pg_sleep(1000000) from gp_dist_random('gp_id'); 600是一个非标准的priority，因此显示NON-STANDARD postgres=# select gp_adjust_priority(21,1,600); postgres=# select * from gp_toolkit.gp_resq_priority_statement; rqpdatname | rqpusename | rqpsession | rqpcommand | rqppriority | rqpweight | rqpquery ------------+------------+------------+------------+--------------+-----------+-------------------------------------------------------- postgres | digoal | 21 | 1 | NON-STANDARD | 600 | select pg_sleep(1000000) from gp_dist_random('gp_id'); 

代码以下

/**
 * An interface to re-weigh an existing session on the master and all backends. * Input: * session id - what session is statement on? * command count - what is the command count of statement. * priority value - text, what should be the new priority of this statement. * Output: * number of backends whose weights were changed by this call. */ Datum gp_adjust_priority_value(PG_FUNCTION_ARGS) { int32 session_id = PG_GETARG_INT32(0); int32 command_count = PG_GETARG_INT32(1); Datum dVal = PG_GETARG_DATUM(2); char *priorityVal = NULL; int wt = 0; priorityVal = DatumGetCString(DirectFunctionCall1(textout, dVal)); if (!priorityVal) { elog(ERROR, "Invalid priority value specified."); } wt = BackoffPriorityValueToInt(priorityVal); Assert(wt > 0); pfree(priorityVal);  return DirectFunctionCall3(gp_adjust_priority_int, Int32GetDatum(session_id), Int32GetDatum(command_count), Int32GetDatum(wt)); } 

经过cgroup细粒度控制query的资源使用

前面讲的是Greenplum经过自带的resource queue来控制资源使用的状况，可是Greenplum控制的资源种类有限，有没有更细粒度的控制方法呢？

若是要进行更细粒度的控制，能够考虑使用cgroup来隔离各个query的资源使用。

能够作到对cpu, memory, iops, network的细粒度控制。

作法也很简单，
首先要在全部的物理主机建立对应的cgroup，例如为每一个资源分配几个等级。

• cpu: 分若干个等级
• memory: 分若干个等级
• iops: 分若干个等级
• network: 分若干个等级

而后得到会话对应的全部节点的backend pid，将backend pid move到对应的cgroup便可。

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（原文地址：https://yq.aliyun.com/articles/57763）